796 resultados para Oil pipeline


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The paper presents the development of a decision support system for the management of geotechnical and environmental risks in oil pipelines using a geographical information system. The system covers a 48.5 km long section of the So Paulo to Brasilia (OSBRA) oil pipeline, which crosses three municipalities in the northeast region of the So Paulo state (Brazil) and represents an area of 205.8 km(2). The spatial database was created using geo-processing procedures, surface and intrusive investigations and geotechnical reports. The risk assessment was based mainly on qualitative models (relative numeric weights and multicriteria decision analysis) and considered pluvial erosion, slope movements, soil corrosion and third party activities. The maps were produced at a scale of 1:10,000.

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The Keystone XL has a big role for transforming Canadian oil to the USA. The function of the pipeline is decreasing the dependency of the American oil industry on other countries and it will help to limit external debt. The proposed pipeline seeks the most suitable route which cannot damage agricultural and natural water recourses such as the Ogallala Aquifer. Using the Geographic Information System (GIS) techniques, the suggested path in this study got extremely high correct results that will help in the future to use the least cost analysis for similar studies. The route analysis contains different weighted overlay surfaces, each, was influenced by various criteria (slope, geology, population and land use). The resulted least cost path routes for each weighted overlay surface were compared with the original proposed pipeline and each displayed surface was more effective than the proposed Keystone XL pipeline.

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Conventionally, oil pipeline projects are evaluated thoroughly by the owner before investment decision is made using market, technical and financial analysis sequentially. The market analysis determines pipelines throughput and supply and demand points. Subsequent, technical analysis identifies technological options and economic and financial analysis then derives the least cost option among all technically feasible options. The subsequent impact assessment tries to justify the selected option by addressing environmental and social issues. The impact assessment often suggests alternative sites, technologies, and/or implementation methodology, necessitating revision of technical and financial analysis. This study addresses these issues via an integrated project evaluation and selection model. The model uses analytic hierarchy process, a multiple-attribute decision-making technique. The effectiveness of the model has been demonstrated through a case application on cross-country petroleum pipeline project in India.

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Chromosomal aberration (CA) assays have been widely used, not only to assess the genotoxic effects of chemical agents, but also to evaluate their action mechanisms on the genetic material of exposed organisms. This is of particular interest, since such analyses provide a better knowledge related to the action of these agents on DNA. Among test organisms, Allium cepa is an outstanding species due to its sensitivity and suitable chromosomal features, which are essential for studies on chromosomal damage or disturbances in cell cycle. The goal of the present study was to analyze the action mechanisms of chemical agents present in petroleum polluted waters. Therefore, CA assay was carried out in A. cepa meristematic cells exposed to the Guaeca river waters, located in the city of Sao Sebastiao, SP, Brazil, which had its waters impacted by an oil pipeline leak. Analyses of the aberration types showed clastogenic and aneugenic effects for the roots exposed to the polluted waters from Guaeca river, besides the induction of cell death. Probably all the observed effects were induced by the petroleum hydrocarbons derived from the oil leakage. (C) 2008 Elsevier B.V. All rights reserved.

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The modern technology of materials and structural integrity of pipelines requests the use of inspection tools named inspection pigs to detect, localize and measure the length, width and depth dimensions of the thickness losses of walls of buried and underwater pipelines in service. These tools run them internally, performing and recording measurements, with performance that varies according to the pig s technology. It has been developed recently an instrumented pig technology, called feller pig. This work aims to indicate factors that influence the feller pig technology performance in the detection and in the accuracy of measurement of the length, width and depth dimensions of the thickness losses on the internal surface of an oil pipeline wall under normal conditions of oil pipe inspection with pig. In this work, is made a collection of factors and an analyses of the technology based on the available literature, as well as an experiment to observe the technology and the factors operating. In the experiment, a feeler pig is used in a pipeline built in carbon steel and in operation that flows petroleum, in witch are observed areas with internal thickness losses occurred naturally. Some of these areas and their dimensions taken by automated ultra-sound scanner are compared with the ones indicated by the feller pig. Based on the data collection, on the analysis and on the experiment, the influence of factors object of this research is discussed. It is concluded that, among these, there are factors related to pipe fabrication tolerances, to wear of pig components, to internal adhesive wear of pipeline, to other pipeline damages and to technology characteristics. Finally, actions are suggested to know better, improve and define the applicability of this technology

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In the present study, we applied Chromosome Aberration (CA) and Micronucleus (MN) tests to Allium cepa root cells, in order to evaluate the water quality of Guaeca river. This river, located in the city of Sao Sebastiao, SP, Brazil, had been affected by an oil pipeline leak. Chemical analyses of Total Petroleum Hydrocarbons (TPHs) and Polycyclic Aromatic Hydrocarbons (PAHs) were also carried out in water samples, collected in July 2005 (dry season) and February 2006 (rainy season) in 4 different river sites. The largest CA and MN incidence in the meristematic cells of A. cepa was observed after exposure to water sample collected during the dry season, at the spring of the river, where the oil leak has arisen. The F, cells from roots exposed to such sample (non-merismatic region) were also analyzed for the incidence of MN, showing a larger frequency of irregularities, indicating a possible development of CA into MN. Lastly, our study reveals a direct correlation between water chemical analyses (contamination by TPHs and PAHs) and both genotoxic and mutagenic effects observed in exposed A. cepa cells. (C) 2007 Elsevier B.V. All rights reserved.

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Taking into account the consistent and important expansion of the Brazilian oil and gas pipelines network in the last years, this work discusses how these lines are planned regarding the continental environmental context. Its central objective is to show how studies to select alternative lines are made before a gas or oil pipeline is installed. These studies help to choose routes in which the environment is less affected by the pipeline, and use specific methods, technologies, and tools. Bibliographical studies combined with interviews and discussions with experts of the oil and gas sector were used to support this monograph.

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Bulgaria and Russia are entering the final phase of setting the conditions of their co-operation in the energy sector. A new gas contract is being negotiated because the currently applicable agreements will have expired by the end of 2012. The fate of two major energy projects – whose implementation depends on good co-operation between Sofia and Moscow: the Burgas– –Alexandroupolis oil pipeline and the construction of a Bulgarian nuclear power plant in Belene with Russian participation – is currently being decided. Another issue ever-present on the agenda is the future of the South Stream gas pipeline promoted by Russia, which is to run through Bulgarian territory. The outcome of all the aforementioned discussions and negotiations will determine for years the model of Bulgarian-Russian relations and may strongly affect the shape of the oil, gas and electricity markets in South-Eastern Europe.

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There is currently considerable interest in developing general non-linear density models based on latent, or hidden, variables. Such models have the ability to discover the presence of a relatively small number of underlying `causes' which, acting in combination, give rise to the apparent complexity of the observed data set. Unfortunately, to train such models generally requires large computational effort. In this paper we introduce a novel latent variable algorithm which retains the general non-linear capabilities of previous models but which uses a training procedure based on the EM algorithm. We demonstrate the performance of the model on a toy problem and on data from flow diagnostics for a multi-phase oil pipeline.

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Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis which is based on a linear transformations between the latent space and the data space. In this paper we introduce a form of non-linear latent variable model called the Generative Topographic Mapping, for which the parameters of the model can be determined using the EM algorithm. GTM provides a principled alternative to the widely used Self-Organizing Map (SOM) of Kohonen (1982), and overcomes most of the significant limitations of the SOM. We demonstrate the performance of the GTM algorithm on a toy problem and on simulated data from flow diagnostics for a multi-phase oil pipeline.

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The Self-Organizing Map (SOM) algorithm has been extensively studied and has been applied with considerable success to a wide variety of problems. However, the algorithm is derived from heuristic ideas and this leads to a number of significant limitations. In this paper, we consider the problem of modelling the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. We introduce a novel form of latent variable model, which we call the GTM algorithm (for Generative Topographic Mapping), which allows general non-linear transformations from latent space to data space, and which is trained using the EM (expectation-maximization) algorithm. Our approach overcomes the limitations of the SOM, while introducing no significant disadvantages. We demonstrate the performance of the GTM algorithm on simulated data from flow diagnostics for a multi-phase oil pipeline.

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Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis which is based on a linear transformations between the latent space and the data space. In this paper we introduce a form of non-linear latent variable model called the Generative Topographic Mapping, for which the parameters of the model can be determined using the EM algorithm. GTM provides a principled alternative to the widely used Self-Organizing Map (SOM) of Kohonen (1982), and overcomes most of the significant limitations of the SOM. We demonstrate the performance of the GTM algorithm on a toy problem and on simulated data from flow diagnostics for a multi-phase oil pipeline.

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The evaluation and selection of industrial projects before investment decision is customarily done using marketing, technical and financial information. Subsequently, environmental impact assessment and social impact assessment are carried out mainly to satisfy the statutory agencies. Because of stricter environment regulations in developed and developing countries, quite often impact assessment suggests alternate sites, technologies, designs, and implementation methods as mitigating measures. This causes considerable delay to complete project feasibility analysis and selection as complete analysis requires to be taken up again and again till the statutory regulatory authority approves the project. Moreover, project analysis through above process often results sub-optimal project as financial analysis may eliminate better options, as more environment friendly alternative will always be cost intensive. In this circumstance, this study proposes a decision support system, which analyses projects with respect to market, technicalities, and social and environmental impact in an integrated framework using analytic hierarchy process, a multiple-attribute decision-making technique. This not only reduces duration of project evaluation and selection, but also helps select optimal project for the organization for sustainable development. The entire methodology has been applied to a cross-country oil pipeline project in India and its effectiveness has been demonstrated. © 2005 Elsevier B.V. All rights reserved.

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Risks and uncertainties are part and parcel of any project as projects are planned with many assumptions. Therefore, managing those risks is the key to project success. Although risk is present in all most all projects, large-scale construction projects are most vulnerable. Risk is by nature subjective. However, managing risk subjectively posses the danger of non-achievement of project goals. This study introduces an analytical framework for managing risk in projects. All the risk factors are identified, their effects are analyzed, and alternative responses are derived with cost implication for mitigating the identified risks. A decision-making framework is then formulated using decision tree. The expected monetary values are derived for each alternative. The responses, which require least cost is selected. The entire methodology has been explained through a case study of an oil pipeline project in India and its effectiveness in managing projects has been demonstrated. © INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING.